Hear a panel of HR leaders engage in a conversation about how they are using predictive and prescriptive analytics applied to talent to drive better business results. Join us to explore how you can be more effective in designing pilots, demonstrating the ROI for talent analytics, and translating talent data into talent insight.
In today's economic downturn, organizations are looking for ways to improve the way they do business to keep ahead of the competition and improve revenue. Increasingly, organizations are finding that the benefits of BI can be complemented when combined with predictive analysis.
Published By: Mintigo
Published Date: Sep 05, 2018
One of the most common use cases for AI in B2B is to make predictions about which accounts are most likely to buy and which leads are most likely to convert. However, use cases for AI are being extended beyond predictive account and lead scoring to include decision-making and process automation as well. Download this SiriusDecisions technology perspective on Predictive Analytics and Artificial Intelligence Technology to learn more.
This paper will cover:
• The benefits, evolution and capabilities of AI technology solutions for B2B organizations
• The core and extended capability groups of AI
• The business priorities supported by AI
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Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud
and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes
need not stand in the way. To explore the challenges, opportunities, and value of tax fraud analytics, IIA spoke with Deborah Pianko, a
Government Fraud Solutions Architect within the SAS Security Intelligence practice.
This paper will outline the value and methods involved in data mining across both quantitative and qualitative data. In addition, it will describe the data transformations necessary before doing such work, and the tools that are particularly valuable for mining mixed data types.
Learn what criteria distinguished certain companies as top performers within the SMB sector, the factors to consider when assessing your organization's BI competency and the required actions to achieve best-in-class performance.
This paper defines predictive analytics, then details ways this type of analytics can be applied to marketing, risk, operations and more. It also includes information relevant to a wide variety of industries - from manufacturing to hospitals.
The solution to operationalizing analytic s involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.
This paper will discuss the barriers to data-driven decision making for midsized businesses, and how experts and non-experts alike can use SAS Visual Analytics to unlock the value of data – including big data – to increase revenue, cut operational costs and better manage their business.
Driving the right interaction at the right time.
This paper examines the need to uncover and understand the
dynamic roles and changing needs of individuals, the importance of synchronizing interactions with these changing needs, and how predictive analytics can drive customer intimacy by facilitating 1:1 interactions.